AmazonPosted 2d ago
Applied Scientist II, Amazon Supply Chain at Amazon scores 90 out of 100 on AI centrality, which makes it AI Level 4 of 4 (Builds AI) on this board. The level measures how much of the work is AI, not seniority.
AI in this role
Design and deploy machine learning, generative AI, and agentic AI models to power intelligent supply chain applications at global scale.
We are looking for an Applied Scientist to join our team that is building enterprise applications leveraging machine learning, generative AI, and agentic AI to help millions of companies worldwide manage their day-to-day supply chain operations. Our mission is to accelerate our customers' businesses through intuitive, differentiated technology solutions that solve enduring supply chain challenges. We blend strategic vision with curiosity and Amazon's real-world operational experience to build opinionated, turnkey solutions that make the 'buy versus build' decision a no-brainer for our customers.
As an Applied Scientist, you will design and develop machine learning models and algorithms that power intelligent supply chain applications at global scale. You will work at the intersection of research and real-world product impact, translating scientific advances into production systems that serve millions of customers. We operate like a startup within AWS, offering you the opportunity to tackle complex challenges while working with the latest technologies in deep learning, large language models, and optimization.
If you are passionate about pushing the boundaries of applied science, thrive in ambiguous problem spaces, and want to shape the future of supply chain intelligence while having the backing of AWS's extensive resources, we want to hear from you.
Key job responsibilities
- Design, develop, and deploy machine learning models for demand forecasting, inventory optimization, anomaly detection, and supply chain decision-making.
- Develop generative AI and agentic AI solutions that automate complex supply chain workflows and deliver intelligent, adaptive recommendations to customers.
- Formulate real-world business problems as machine learning problems; define data requirements, model architectures, evaluation metrics, and experimentation frameworks.
- Drive end-to-end applied science projects from ideation through experimentation, offline evaluation, A/B testing, and production deployment at scale.
- Collaborate with engineering, product management, and business stakeholders to translate scientific capabilities into customer-facing product features, and mentor other scientists to raise the team's technical bar.
A day in the life
You will start many mornings reviewing experiment results and model metrics before joining a science sync where you and your teammates discuss progress, debug tricky modeling issues, and brainstorm new approaches. From there, you may spend focused time writing and testing model code in Python or PyTorch, running offline evaluations, or preparing an A/B test for a new forecasting algorithm. Expect regular working sessions with software engineers to integrate your models into production services, and occasional deep-dive reviews where you present your scientific approach and findings to the broader team.
About the team
The AWS Applied AI Solutions team builds enterprise applications that leverage Amazon's operational expertise to solve real-world supply chain challenges for millions of companies. We operate like a startup within AWS, moving fast and shipping iteratively with modern AI technologies. We invest in your growth through mentorship from experienced scientists, conference publication support, and internal science reading groups. If your career hasn't followed a traditional path, we encourage you to apply — we value varied experiences and perspectives.
Basic qualifications
- 3+ years of building models for business application experience
- PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
- Experience programming in Java, C++, Python or related language
Preferred qualifications
- Experience using Unix/Linux
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.
USA, WA, Seattle - 142,800.00 - 193,200.00 USD annually
Prepare for this job
A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.
Skills and AI tools this role asks for
Questions you could be asked
- Tell me about a project where machine learning was part of your work. What did you do?
- Tell me about a project where generative ai was part of your work. What did you do?
- Tell me about a project where agentic ai was part of your work. What did you do?
- Tell me about a project where supply chain was part of your work. What did you do?
- Tell me about a project where demand forecasting was part of your work. What did you do?
Adapt your resume
- List these exact terms on your resume: Machine Learning, Generative AI, Agentic AI, Supply Chain, and Demand Forecasting. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.
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